1. Field of the Invention
The present invention relates to processing of seismic data representative of subsurface features in the earth during geophysical exploration.
2. Description of the Prior Art
Seismic recordings acquired in oil and gas exploration are sampled in both space (X) and time (T). This is conventionally referred to as the offset-time or X-T domain. The offset-time ensemble can be common source, common receiver, common midpoint or common offset gathers. Seismic signal energy in the seismic recordings is composed of both a reflection signal component and unwanted noise.
In the X-T domain, noise and reflection signal commonly overlap, with noise masking or interfering with the reflection signal component of interest concerning subsurface features. It has been common practice to transform the recordings from the offset-time, or X-T, domain to another domain for noise attenuation and reflection signal enhancement. The frequency-wavenumber (or F-K) domain and the time-slowness (or TAU-P) domain are ones into which seismic recordings were often transformed from the time-offset domain for these purposes. Appropriate filters have been designed for operation in these domains to attenuate noise and enhance reflection signals.
Prior to transformation from the original or time-offset domain to another domain, it has been conventional to employ amplitude balancing of the signal energy. The signal amplitudes, as has been set forth above, have two components. The first is the meaningful reflection information component of interest as to subsurface structure. The second amplitude component is composed of unwanted noise or amplitude variations which may arise due to a number of factors or effects. Examples include non-uniform array weighting and variations either in geophone coupling or in surface layer material. When amplitude balancing was done before data transformation to another domain, the unwanted amplitude variations were transformed along with the reflection signal of interest. This caused repetitive pattern dispersion in the transformed data, often masking or hiding meaningful information present in the data.